Range measurement using multiple coded apertures

ABSTRACT

A method of using an image capture device to identify range information for objects in a scene includes providing an image capture device having an image sensor, at least two coded apertures, and a lens; storing in a memory a set of blur parameters derived from range calibration data for each coded aperture; capturing images of the scene having a plurality of objects using each of the coded apertures; providing a set of deblurred images using the captured images from each coded aperture and each of the blur parameters from the stored set; and using the set of deblurred images to determine the range information for the objects in the scene.

CROSS REFERENCE TO RELATED APPLICATIONS

Reference is made to commonly assigned, co-pending U.S. patentapplication Ser. No. 12/612,135, filed Nov. 4, 2009, entitled Imagedeblurring using a combined differential image, by Sen Weng, et al,co-pending U.S. patent application Ser. No. 12/770,810, filedconcurrently herewith and entitled “Range measurement using codedaperture”, by Paul J. Kane, et al, co-pending U.S. patent applicationSer. No. 12/770,830 filed concurrently herewith and entitled Rangemeasurement using a zoom camera, by Paul J. Kane, et al, co-pending U.S.patent application Ser. No. 12/770,894, filed concurrently herewith andentitled Digital camera with coded aperture rangefinder, by Paul J.Kane, et al, and co-pending U.S. patent application Ser. No. 12/770,919,filed concurrently herewith and entitled Range measurement usingsymmetric coded apertures, by Paul J. Kane, et al, all of which areincorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to an image capture device that is capableof determining range information for objects in a scene, and inparticular a capture device that uses coded apertures and computationalalgorithms to efficiently determine the range information.

BACKGROUND OF THE INVENTION

Optical imaging systems are designed to create a focused image of sceneobjects over a specified range of distances. The image is in sharpestfocus in a two dimensional (2D) plane in the image space, called thefocal or image plane. From geometrical optics, a perfect focalrelationship between a scene object and the image plane exists only forcombinations of object and image distances that obey the thin lensequation:

$\begin{matrix}{\frac{1}{f} = {\frac{1}{s} + \frac{1}{s^{\prime}}}} & (1)\end{matrix}$where f is the focal length of the lens, s is the distance from theobject to the lens, and s′ is the distance from the lens to the imageplane. This equation holds for a single thin lens, but it is well knownthat thick lenses, compound lenses and more complex optical systems aremodeled as a single thin lens with an effective focal length f.Alternatively, complex systems are modeled using the construct ofprincipal planes, with the object and image distances s, s′ measuredfrom these planes, and using the effective focal length in the aboveequation, hereafter referred to as the lens equation.

It is also known that once a system is focused on an object at distances₁, in general only objects at this distance are in sharp focus at thecorresponding image plane located at distance s₁′. An object at adifferent distance s₂ produces its sharpest image at the correspondingimage distance s₂′, determined by the lens equation. If the system isfocused at s₁, an object at s₂ produces a defocused, blurred image atthe image plane located at s₁′. The degree of blur depends on thedifference between the two object distances, s₁ and s₂, the focal lengthf of the lens, and the aperture of the lens as measured by the f-number,denoted f/#. For example, FIG. 1 shows a single lens 10 having clearaperture of diameter D. The on-axis point P₁ of an object located atdistance s₁ is imaged at point P₁′ at distance s₁′ from the lens. Theon-axis point P₂ of an object located at distance s₂ is imaged at pointP₂′ at distance s₂′ from the lens. Tracing rays from these objectpoints, axial rays 20 and 22 converge on image point P₁′, while axialrays 24 and 26 converge on image point P₂′, then intercept the imageplane of P₁′ where they are separated by a distance d. In an opticalsystem with circular symmetry, the distribution of rays emanating fromP₂ over all directions results in a circle of diameter d at the imageplane of P₁′, which is called the blur circle or circle of confusion.

On-axis point P₁ moves farther from the lens, tending towards infinity,it is clear from the lens equation that s₁′=f. This leads to the usualdefinition of the f-number as f/#=f/D. At finite distances, the workingf-number is defined as (f/#)_(w)=f/s′₁. In either case, it is clear thatthe f-number is an angular measure of the cone of light reaching theimage plane, which in turn is related to the diameter of the blur circled. In fact, it is shown that

$\begin{matrix}{d = {\frac{f}{\left( {f/\#} \right)s_{2}^{\prime}}{{{s_{2}^{\prime} - s_{1}^{\prime}}}.}}} & (2)\end{matrix}$

By accurate measure of the focal length and f-number of a lens, and thediameter d of the blur circle for various objects in a two dimensionalimage plane, in principle it is possible to obtain depth information forobjects in the scene by inverting the Eq. (2), and applying the lensequation to relate the object and image distances. This requires carefulcalibration of the optical system at one or more known object distances,at which point the remaining task is the accurate determination of theblur circle diameter d.

The above discussion establishes the basic principles behind passiveoptical ranging methods based on focus. That is, methods based onexisting illumination (passive) that analyze the degree of focus ofscene objects, and relate this to their distance from the camera. Suchmethods are divided into two categories: depth from defocus methodsassume that the camera is focused once, and that a single image iscaptured and analyzed for depth, whereas depth from focus methods assumethat multiple images are captured at different focus positions, and theparameters of the different camera settings are used to infer the depthof scene objects.

The method presented above provides insight into the problem of depthrecovery, but unfortunately is oversimplified and not robust inpractice. Based on geometrical optics, it predicts that the out-of-focusimage of each object point is a uniform circular disk or blur circle. Inpractice, diffraction effects and lens aberrations lead to a morecomplicated light distribution, characterized by a point spread function(psf), specifying the intensity of the light at any point (x,y) in theimage plane due to a point light source in the object plane. Asexplained by Bove (V. M. Bove, Pictorial Applications for Range SensingCameras, SPIE vol. 901, pp. 10-17, 1988), the defocusing process is moreaccurately modeled as a convolution of the image intensities with adepth-dependent psf:i _(def)(x,y;z)=i(x,y)*h(x,y;z),  (3)where i_(def)(x,y;z) is the defocused image, i(x,y) is the in-focusimage, h(x,y;z) is the depth-dependent psf and * denotes convolution. Inthe Fourier domain, this is written:I _(def)(v _(x) ,v _(y))=I(v _(x) ,v _(y))H(v _(x) ,v _(y) ;z),  (4)where I_(def)(v_(x),v_(y)) is the Fourier transform of the defocusedimage, I(v_(x),v_(y)) is the Fourier transform of the in-focus image,and H(v_(x),v_(;),z) is the Fourier transform of the depth-dependentpsf. Note that the Fourier Transform of the psf is the Optical TransferFunction, or OTF. Bove describes a depth-from-focus method, in which itis assumed that the psf is circularly symmetric, i.e. h(x,y;z)=h(r;z)and H(v_(x),v_(y);z)=H(ρ;z), where r and ρ are radii in the spatial andspatial frequency domains, respectively. Two images are captured, onewith a small camera aperture (long depth of focus) and one with a largecamera aperture (small depth of focus). The Discrete Fourier Transform(DFT) is taken of corresponding windowed blocks in the two images,followed by a radial average of the resulting power spectra, meaningthat an average value of the spectrum is computed at a series of radialdistances from the origin in frequency space, over the 360 degree angle.At that point the radially averaged power spectra of the long and shortdepth of field (DOF) images are used to compute an estimate for H(ρ,z)at corresponding windowed blocks, assuming that each block represents ascene element at a different distance z from the camera. The system iscalibrated using a scene containing objects at known distances [z₁, z₂,. . . z_(n)] to characterize H(ρ;z), which then is related to the blurcircle diameter. A regression of the blur circle diameter vs. distance zthen leads to a depth or range map for the image, with a resolutioncorresponding to the size of the blocks chosen for the DFT.

Methods based on blur circle regression have been shown to producereliable depth estimates. Depth resolution is limited by the fact thatthe blur circle diameter changes rapidly near focus, but very slowlyaway from focus, and the behavior is asymmetric with respect to thefocal position. Also, despite the fact that the method is based onanalysis of the point spread function, it relies on a single metric(blur circle diameter) derived from the psf.

Other depth from defocus methods seek to engineer the behavior of thepsf as a function of defocus in a predictable way. By producing acontrolled depth-dependent blurring function, this information is usedto deblur the image and infer the depth of scene objects based on theresults of the deblurring operations. There are two main parts to thisproblem: the control of the psf behavior, and deblurring of the image,given the psf as a function of defocus. The psf behavior is controlledby placing a mask into the optical system, typically at the plane of theaperture stop. For example, FIG. 2 shows a schematic of an opticalsystem from the prior art with two lenses 30 and 34, and a binarytransmittance mask 32 including an array of holes, placed in between. Inmost cases, the mask is the element in the system that limits the bundleof light rays that propagate from an axial object point, and istherefore by definition the aperture stop. If the lenses are reasonablyfree from aberrations, the mask, combined with diffraction effects, willlargely determine the psf and OTF (see J. W. Goodman, Introduction toFourier Optics, McGraw-Hill, San Francisco, 1968, pp. 113-117). Thisobservation is the working principle behind the encoded blur or codedaperture methods. In one example of the prior art, Veeraraghavan et al(Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fieldsand Coded Aperture Refocusing, ACM Transactions on Graphics 26 (3), July2007, paper 69) demonstrate that a broadband frequency mask composed ofsquare, uniformly transmitting cells can preserve high spatialfrequencies during defocus blurring. By assuming that the defocus psf isa scaled version of the aperture mask, a valid assumption whendiffraction effects are negligible, the authors show that depthinformation is obtained by deblurring. This requires solving thedeconvolution problem, i.e. inverting Eq. (3) to obtain h(x,y;z) for therelevant values of z. In principle, it is easier to invert the spatialfrequency domain counterpart of this equation, namely Eq. (4) which isdone at frequencies for which H(v_(x),v_(;),z) is nonzero.

In practice, finding a unique solution for deconvolution is well knownas a challenging problem. Veeraraghavan et al. solve the problem byfirst assuming the scene is composed of discrete depth layers, and thenforming an estimate of the number of layers in the scene. Then, thescale of the psf is estimated for each layer separately, using the modelh(x,y,z)=m(k(z)x/w,k(z)y/w),  (5)where m(x,y) is the mask transmittance function, k(z) is the number ofpixels in the psf at depth z, and w is the number of cells in the 2Dmask. The authors apply a model for the distribution of image gradients,along with Eq. (5) for the psf, to deconvolve the image once for eachassumed depth layer in the scene. The results of the deconvolutions aredesirable only for those psfs whose scale they match, thereby indicatingthe corresponding depth of the region. These results are limited inscope to systems behaving according to the mask scaling model of Eq.(5), and masks composed of uniform, square cells.

Levin et al (Image and Depth from a Conventional Camera with a CodedAperture, ACM Transactions on Graphics 26 (3), July 2007, paper 70)follow a similar approach to Veeraraghavan, however, Levin et al rely ondirect photography of a test pattern at a series of defocused imageplanes, to infer the psf as a function of defocus. Also, Levin et al.investigated a number of different mask designs in an attempt to arriveat an optimum coded aperture. They assume a Gaussian distribution ofsparse image gradients, along with a Gaussian noise model, in theirdeconvolution algorithm. Therefore, the coded aperture solution isdependent on assumptions made in the deconvolution analysis.

SUMMARY OF THE INVENTION

The present invention represents a method for using an image capturedevice to identify range information for objects in a scene, comprising:

a) providing an image capture device having an image sensor, at leasttwo coded apertures, and a lens;

b) storing in a memory a set of blur parameters derived from rangecalibration data for each coded aperture;

c) capturing images of the scene having a plurality of objects usingeach of the coded apertures;

d) providing a set of deblurred images using the captured images fromeach coded aperture and each of the blur parameters from the stored set;and

e) using the set of deblurred images to determine the range informationfor the objects in the scene.

This invention has the advantage that it produces range estimates basedon capture devices with two or more coded apertures, which has increasedflexibility of operation and produces improved range estimates.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of a single lens optical system as known in theprior art.

FIG. 2 is a schematic of an optical system with a coded aperture mask asknown in the prior art.

FIG. 3 is a flow chart showing the steps of a method of using an imagecapture device to identify range information for objects in a sceneaccording to one embodiment of the present invention.

FIG. 4 is a schematic of a capture device according to one embodiment ofthe present invention.

FIG. 5 is a schematic of an optical system with two coded aperturesaccording to one embodiment of the present invention.

FIG. 6 is a schematic of a laboratory setup for obtaining blurparameters for one object distance and a series of defocus distancesaccording to one embodiment of the present invention.

FIG. 7 is a process diagram illustrating how a captured image and blurparameters are used to provide a set of deblurred images, according toone embodiment of the present invention.

FIG. 8 is a process diagram illustrating the deblurring of a singleimage according to one embodiment of the present invention.

FIG. 9 is a schematic showing an array of indices centered on a currentpixel location according to one embodiment of the present invention.

FIG. 10 is a process diagram illustrating a deblurred image setprocessed to determine the range information for objects in a scene,according to one embodiment of the present invention.

FIG. 11 is a process diagram illustrating a deblurred image setprocessed to determine the range information for objects in a scene,according to an alternate embodiment of the present invention.

FIG. 12 is a schematic of a digital camera system according to oneembodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, some embodiments of the present inventionwill be described in terms that would ordinarily be implemented assoftware programs. Those skilled in the art will readily recognize thatthe equivalent of such software can also be constructed in hardware.Because image manipulation algorithms and systems are well known, thepresent description will be directed in particular to algorithms andsystems forming part of, or cooperating more directly with, the methodin accordance with the present invention. Other aspects of suchalgorithms and systems, together with hardware and software forproducing and otherwise processing the image signals involved therewith,not specifically shown or described herein are selected from suchsystems, algorithms, components, and elements known in the art. Giventhe system as described according to the invention in the following,software not specifically shown, suggested, or described herein that isuseful for implementation of the invention is conventional and withinthe ordinary skill in such arts.

The invention is inclusive of combinations of the embodiments describedherein. References to “a particular embodiment” and the like refer tofeatures that are present in at least one embodiment of the invention.Separate references to “an embodiment” or “particular embodiments” orthe like do not necessarily refer to the same embodiment or embodiments;however, such embodiments are not mutually exclusive, unless soindicated or as are readily apparent to one of skill in the art. The useof singular or plural in referring to the “method” or “methods” and thelike is not limiting. It should be noted that, unless otherwiseexplicitly noted or required by context, the word “or” is used in thisdisclosure in a non-exclusive sense.

FIG. 3 is a flow chart showing the steps of a method of using an imagecapture device to identify range information for objects in a sceneaccording to an embodiment of the present invention. The method includesthe steps of: providing an image capture device 50 having an imagesensor, at least two coded apertures, and a lens; storing in a memory 60a set of blur parameters derived from range calibration data for eachcoded aperture; capturing images of the scene 70 having a plurality ofobjects using each of the coded apertures, providing a set of deblurredimages 80 using the capture image and each of the blur parameters fromthe stored set; and using the set of blurred images to determine therange information 90 for objects in the scene.

An image capture device includes one or more image capture devices thatimplement the methods of the various embodiments of the presentinvention, including the example image capture devices described herein.The phrases “image capture device” or “capture device” are intended toinclude any device including a lens which forms a focused image of ascene at an image plane, wherein an electronic image sensor is locatedat the image plane for the purposes of recording and digitizing theimage, and which further includes a coded aperture or mask locatedbetween the scene or object plane and the image plane. These include adigital camera, cellular phone, digital video camera, surveillancecamera, web camera, television camera, multimedia device, or any otherdevice for recording images. FIG. 4 shows a side view schematic of onesuch capture device according to one embodiment of the presentinvention. The capture device 40 includes a lens 42, shown here as acompound lens composed of multiple elements, a coded aperture plate 44,and an electronic sensor array 46. Preferably, the coded aperturemounting plate 44 is located at the aperture stop of the optical system,or one of the images of the aperture stop, which are known in the art asthe entrance and exit pupils. This can necessitate placement of thecoded aperture in between elements of a compound lens 42, as illustratedin FIG. 3, depending on the location of the aperture stop. FIG. 5 showsan expanded view of the compound lens 42 and the coded aperture mountingplate 44, according to one embodiment of the present invention. As shownin FIG. 5, two coded apertures 43 and 45 are inserted into circularopenings in the plate, which is translated in the horizontal directionto permit placement of either aperture 43 or 45 into the optical path.It will be appreciated that other mounting methods are possible withinthe scope of the invention. For example, the apertures are inserted intoa circular mounting plate so that the apertures are rotated intoposition. It will also be appreciated that the aperture mounting plateis fabricated from many different materials, including, but not limitedto metal, plastic or cardboard. The coded apertures 43 and 45 are of thelight absorbing type, so as to alter only the amplitude distributionacross the optical wavefronts incident upon it, or the phase type, so asto alter only the phase delay across the optical wavefronts incidentupon it, or of mixed type, so as to alter both the amplitude and phase.

Returning to FIG. 3, the step of storing in a memory 60 a set of blurparameters refers to storing a representation of the psf of the imagecapture device for a series of object distances and defocus distances.Storing the blur parameters includes storing a digitized representationof the psf, specified by discrete code values in a two dimensionalmatrix. It also includes storing mathematical parameters derived from aregression or fitting function that has been applied to the psf data,such that the psf values for a given (x,y,z) location are readilycomputed from the parameters and the known regression or fittingfunction. Such memory can include computer disk, ROM, RAM or any otherelectronic memory known in the art. Such memory can reside inside thecamera, or in a computer or other device electronically linked to thecamera. In the embodiment shown in FIG. 4, the memory 48 storing blurparameters 47 a, i.e. [p₁, p₂, . . . p_(n)], for the first codedaperture and blur parameters 47 b, i.e. [q₁, w₂, . . . q_(n)], for thesecond coded aperture is located inside the camera 40.

FIG. 6 is a schematic of a laboratory setup for obtaining blurparameters for one object distance and a series of defocus distances inaccord with the present invention. A simulated point source including alight source 200 is focused by condenser optics 210 at a point on theoptical axis intersected by the focal plane F, which is also the planeof focus of the camera 40, located at object distance R₀ from thecamera. The light rays 220 and 230 passing through the point of focusappear to emanate from a point source located on the optical axis atdistance R₀ from the camera. With the first coded aperture in place inthe mounting plate 44, an image of this light is captured by the camera40, thus recording the camera psf of the camera 40 at object distanceR₀. The defocused psf for objects at other distances from the camera 40is captured by moving the source 200 and condenser lens 210 (in thisexample, to the left) together so as to move the location of theeffective point source to other planes, for example D₁ and D₂, whilemaintaining the camera 40 focus position at plane F. The distances (orrange data) from the camera 40 to planes F, D₁ and D₂ are then recordedalong with the psf images to complete the first set of range calibrationdata. The process is repeated with the second coded aperture in place inthe mounting plate 44 to complete the second set of range calibrationdata

Returning to FIG. 3, the step of capturing an image of the scene 70includes capturing two images of the scene, with first and second codedapertures, or two digital image sequences, also known in the art asmotion or video sequences, one image sequence for each of the first andsecond coded apertures. In this way the method includes the ability toidentify range information for one or more moving objects in a scene.This is accomplished by determining range information 90 for each imagein the sequence, or by determining range information for some subset ofimages in the sequence. In some embodiments, a subset of images in thesequence is used to determine range information for one or more movingobjects in the scene, as long as the time interval between the imageschosen is sufficiently small to resolve significant changes in the depthor z-direction. That is, this will be a function of the objects' speedin the z-direction and the original image capture interval, or framerate. In other embodiments, the determination of range information forone or more moving objects in the scene is used to identify stationaryand moving objects in the scene. This is especially advantageous if themoving objects have a z-component to their motion vector, i.e. theirdepth changes with time, or image frame. Stationary objects areidentified as those objects for which the computed range values areunchanged with time after accounting for motion of the camera, whereasmoving objects have range values that can change with time. In yetanother embodiment, the range information associated with moving objectsis used by an image capture device to track such objects.

FIG. 7 shows a process diagram in which a captured image 72 and blurparameters [p₁, p₂, . . . p_(n)] 47 a and [q₁, q₂, . . . q_(n)] 47 bstored in a memory 48 are used to provide 80 a set of deblurred images81 a and 81 b. The blur parameters are a set of two dimensional matricesthat approximate the psf of the image capture device 40 for the distanceat which the image was captured, and a series of defocus distancescovering the range of objects in the scene. Alternatively, the blurparameters are mathematical parameters from a regression or fittingfunction as described above. In either case, a digital representation ofthe point spread functions 49 that span the range of object distances ofinterest in the object space are computed from the blur parameters,represented in FIG. 7 as the set [psf₁₁, psf₂₁, . . . psf_(1m)psf_(2m)].Here the n,m subscripts refer to the m^(th) range value for codedaperture n=1 or n=2. In the preferred embodiment, there is a one-to-onecorrespondence between the blur parameters 47 a, 47 b and the set ofdigitally represented psfs 49. In some embodiments, there is not aone-to-one correspondence. In some embodiments, digitally representedpsfs at defocus distances for which blur parameter data has not beenrecorded is computed by interpolating or extrapolating blur parameterdata from defocus distances for which blur parameter data is available.In the embodiment illustrated here, the sets of range values associatedwith the blur parameter data for the two coded apertures are identical.In other embodiments, blur parameter data is obtained at sets of rangevalues for the two coded apertures that are partially coincident,overlapping but not coincident (i.e. interleaved), or covering disjointintervals with different minimum and maximum range values.

Returning to FIG. 7, the digitally represented psfs 49 are used in adeconvolution operation to provide two sets of deblurred images, thefirst 81 a, resulting from the first set of coded aperture psf data, andthe second 81 b, resulting from the second set of coded aperture psfdata. In the embodiment shown, the captured image 72 which containsscene objects O₁, O₂ and O₃, is deconvolved 2m times, once for each ofthe 2m elements in the set 49, to create a set of 2m deblurred images,81 a and 81 b. The deblurred image sets 81 a and 81 b, whose elementsare denoted [I₁, I₂, . . . I_(m)] and [J₁, J₂, . . . J_(m)], are thenfurther processed with reference to the original captured image 72, todetermine the range information for the scene objects O₁, O₂ and O₃.

The step of providing a set of deblurred images 80 will now be describedin further detail with reference to FIG. 8, which illustrates theprocess of deblurring a single image using a single element of the set49 of psfs in accordance with the present invention. As is known in theart, the image to be deblurred is referred to as the blurred image, andthe psf representing the blurring effects of the camera system isreferred to as the blur kernel. A receive blurred image step 102 is usedto receive the captured image 72 of the scene. Next a receive blurkernel step 105 is used to receive a blur kernel 106 which has beenchosen from the set of psfs 49. The blur kernel 106 is a convolutionkernel that is applied to a sharp image of the scene to produce an imagehaving sharpness characteristics approximately equal to one or moreobjects within the captured image 72 of the scene.

Next, an initialize candidate deblurred image step 104 is used toinitialize a candidate deblurred image 107 using the captured image 72.In a preferred embodiment of the present invention, the candidatedeblurred image 107 is initialized by simply setting it equal to thecaptured image 72. Optionally, any deconvolution algorithm known tothose in the art is used to process the captured image 72 using the blurkernel 106, and the candidate deblurred image 107 is then initialized bysetting it equal to the processed image. Examples of such deconvolutionalgorithms would include conventional frequency domain filteringalgorithms such as the well-known Richardson-Lucy (RL) deconvolutionmethod described in the background section. In other embodiments, wherethe captured image 72 is part of an image sequence, a difference imageis computed between the current and previous image in the imagesequence, and the candidate deblurred image is initialized withreference to this difference image. For example, if the differencebetween successive images in the sequence is currently small, thecandidate deblurred image would not be reinitialized from its previousstate, saving processing time. The reinitialization is saved until asignificant difference in the sequence is detected. In otherembodiments, only selected regions of the candidate deblurred image arereinitialized, if significant changes in the sequence are detected inonly selected regions. In yet another embodiment, the range informationis recomputed for only selected regions or objects in the scene where asignificant difference in the sequence is detected, thus savingprocessing time.

Next, a compute differential images step 108 is used to determine aplurality of differential images 109. The differential images 109 caninclude differential images computed by calculating numericalderivatives in different directions (e.g., x and y) and with differentdistance intervals (e.g., Δx=1, 2, 3). A compute combined differentialimage step 110 is used to form a combined differential image 111 bycombining the differential images 109.

Next, an update candidate deblurred image step 112 is used to compute anew candidate deblurred image 113 responsive to the captured image 72,the blur kernel 106, the candidate deblurred image 107, and the combineddifferential image 111. As will be described in more detail later, in apreferred embodiment of the present invention, the update candidatedeblurred image step 112 employs a Bayesian inference method usingMaximum-A-Posterior (MAP) estimation.

Next, a convergence test 114 is used to determine whether the deblurringalgorithm has converged by applying a convergence criterion 115. Theconvergence criterion 115 is specified in any appropriate way known tothose skilled in the art. In a preferred embodiment of the presentinvention, the convergence criterion 115 specifies that the algorithm isterminated if the mean square difference between the new candidatedeblurred image 113 and the candidate deblurred image 107 is less than apredetermined threshold. Alternate forms of convergence criteria arewell known to those skilled in the art. As an example, the convergencecriterion 115 is satisfied when the algorithm is repeated for apredetermined number of iterations. Alternatively, the convergencecriterion 115 can specify that the algorithm is terminated if the meansquare difference between the new candidate deblurred image 113 and thecandidate deblurred image 107 is less than a predetermined threshold,but is terminated after the algorithm is repeated for a predeterminednumber of iterations even if the mean square difference condition is notsatisfied.

If the convergence criterion 115 has not been satisfied, the candidatedeblurred image 107 is updated to be equal to the new candidatedeblurred image 113. If the convergence criterion 115 has beensatisfied, a deblurred image 116 is set to be equal to the new candidatedeblurred image 113. A store deblurred image step 117 is then used tostore the resulting deblurred image 116 in a processor-accessiblememory. The processor-accessible memory is any type of digital storagesuch as RAM or a hard disk.

In a preferred embodiment of the present invention, the deblurred image116 is determined using a Bayesian inference method withMaximum-A-Posterior (MAP) estimation. Using the method, the deblurredimage 116 is determined by defining an energy function of the form:E(L)=(L

K−B)² +λD(L)  (6)where L is the deblurred image 116, K is the blur kernel 106, B is theblurred image (i. e. captured image 72),

is the convolution operator, D(L) is the combined differential image 111and λ is a weighting coefficient

In a preferred embodiment of the present invention the combineddifferential image 111 is computed using the following equation:

$\begin{matrix}{{D(L)} = {\sum\limits_{j}\;{w_{j}\left( {\partial_{j}L} \right)}^{2}}} & (7)\end{matrix}$where j is an index value, ∂_(j) is a differential operatorcorresponding to the j^(th) index, w_(j) is a pixel-dependent weightingfactor which will be described in more detail later.

The index j is used to identify a neighboring pixel for the purpose ofcalculating a difference value. In a preferred embodiment of the presentinvention difference values are calculated for a 5×5 window of pixelscentered on a particular pixel. FIG. 9 shows an array of indices 300centered on a current pixel location 310. The numbers shown in the arrayof indices 300 are the indices j. For example, an index value of j=6corresponds to a top pixel that is 1 row above and 2 columns to the leftof the current pixel location 310.

The differential operator ∂_(j) determines a difference between thepixel value for the current pixel, and the pixel value located at therelative position specified by the index j. For example, ∂₆S wouldcorrespond to a differential image determined by taking the differencebetween each pixel in the deblurred image L with a corresponding pixelthat is 1 row above and 2 columns to the left. In equation form thiswould be given by:∂_(j) L=L(x,y)−L(x−Δx _(j) ,y−Δy _(j))  (8)where Δx_(j) and Δy_(j) are the column and row offsets corresponding tothe j^(th) index, respectively. It will generally be desirable for theset of differential images ∂_(j)L to include one or more horizontaldifferential images representing differences between neighboring pixelsin the horizontal direction and one or more vertical differential imagesrepresenting differences between neighboring pixels in the verticaldirection, as well as one or more diagonal differential imagesrepresenting differences between neighboring pixels in a diagonaldirection.

In a preferred embodiment of the present invention, the pixel-dependentweighting factor w_(j) is determined using the following equation:w _(j)=(w _(d))_(j)(w _(p))_(j)  (9)where (w_(d))_(j) is a distance weighting factor for the j^(th)differential image, and (w_(p))_(j) is a pixel-dependent weightingfactor for the j^(th) differential image.

The distance weighting factor (w_(d))_(j) weights each differentialimage depending on the distance between the pixels being differenced:(w _(d))_(j) =G(d)  (10)where d=√{square root over (Δx_(j) ²+Δy_(j) ²)} is the distance betweenthe pixels being differenced, and G(•) is weighting function. In apreferred embodiment, the weighting function G(•) falls off as aGaussian function so that differential images with larger distances areweighted less than differential images with smaller distances.

The pixel-dependent weighting factor (w_(p))_(j) weights the pixels ineach differential image depending on their magnitude. For reasonsdiscussed in the aforementioned article “Image and depth from aconventional camera with a coded aperture” by Levin et al., it isdesirable for the pixel-dependent weighting factor w to be determinedusing the equation:(w _(p))_(j)=|∂_(j) L| ^(α−2)•  (11)

where |•| is the absolute value operator and α is a constant (e.g.,0.8). During the optimization process, the set of differential images∂_(j)L is calculated for each iteration using the estimate of Ldetermined for the previous iteration.

The first term in the energy function given in Eq. (6) is an imagefidelity term. In the nomenclature of Bayesian inference, it is oftenreferred to as a “likelihood” term. It is seen that this term will besmall when there is a small difference between the blurred image (i. e.captured image 72) (B) and a blurred version of the candidate deblurredimage (L) which as been convolved with the blur kernel 106 (K).

The second term in the energy function given in Eq. (6) is an imagedifferential term. This term is often referred to as an “image prior.”The second term will have low energy when the magnitude of the combineddifferential image 111 is small. This reflects the fact that a sharperimage will generally have more pixels with low gradient values as thewidth of blurred edges is decreased.

The update candidate deblurred image step 112 computes the new candidatedeblurred image 113 by reducing the energy function given in Eq. (8)using optimization methods that are well known to those skilled in theart. In a preferred embodiment of the present invention, theoptimization problem is formulated as a PDE given by:

$\begin{matrix}{\frac{\partial{E(L)}}{\partial L} = 0.} & (12)\end{matrix}$which is solved using conventional PDE solvers. In a preferredembodiment of the present invention, a PDE solver is used where the PDEis converted to a linear equation form that is solved using aconventional linear equation solver, such as a conjugate gradientalgorithm. For more details on solving PDE solvers, refer to theaforementioned article by Levin et al. It should be noted that eventhough the combined differential image 111 is a function of thedeblurred image L, it is held constant during the process of computingthe new candidate deblurred image 113. Once the new candidate deblurredimage 113 has been determined, it is used in the next iteration todetermine an updated combined differential image 111.

FIG. 10 shows a process diagram in which the deblurred image sets 81 a,81 b are processed to determine the range information 91 for the objectsin the scene, in accord with an embodiment of the present invention. Inthis embodiment, each element of the deblurred image sets 81 a, 81 b isdigitally convolved 92, using algorithms known in the art, with itscorresponding element from the set of digitally represented psfs 49 instep 80. The result is two sets of reconstructed images 82 a, 82 b,whose elements are denoted [ρ₁, ρ₂, . . . ρ_(m)] and [θ₁, θ², . . .θ_(m)]. In theory, each reconstructed image should be an exact match forthe original captured image 72, since the convolution operation is theinverse of the deblurring, or deconvolution operation that was performedearlier. However, because the deconvolution operation is imperfect, noelements of the resulting reconstructed image set 82 a, 82 b are aperfect match for the captured image 72. Scene elements reconstruct withhigher fidelity when processed with psfs corresponding to a distancethat more closely matches the distance of the scene element relative tothe plane of camera focus, whereas scene elements processed with psfscorresponding to distances that differ from the distance of the sceneelement relative to the plane of camera focus exhibit poor fidelity andnoticeable artifacts. With reference to FIG. 10, by comparing 93 thereconstructed image sets 82 a, 82 b with the scene elements in thecaptured image 72, range values 91 are assigned by finding the closestmatches between the scene elements in the captured image 72 and thereconstructed versions of those elements in the reconstructed image sets82 a, 82 b. For example, scene elements O₁, O₂, and O₃ in the capturedimage 72 are compared 93 to their reconstructed versions in each element[ρ₁, ρ₂, . . . ρ_(m)] and [θ₁, θ₂, . . . θ_(m)] of the reconstructedimage sets 82 a, 82 b, and assigned range values 91 of R₁, R₂, and R₃that correspond to the psfs that yield the closest matches.

In a preferred embodiment of the present invention, the deblurred andreconstructed image sets 82 a, 82 b are combined before comparison withthe captured image 72 and assignment of range values. FIG. 11 shows thisprocess, wherein the reconstructed image sets 82 a, 82 b are combined95, followed by comparison 93 of the scene elements in each image setwith those of the captured image 72. Combining the reconstructed imagesets 82 a, 82 b resulting from the deblurred image sets 81 a, 81 b isdefined as the creation of a new image set in which each element is aweighted sum of the corresponding elements of the original sets 82 a, 82b, i.e. those set elements corresponding to the same range value. Thisis written mathematically as:î _(comb) ={w ₁ρ₁(x,y)+w ₂θ₁(x,y); w ₁ρ₂(x,y)+w ₂θ₂(x,y); . . . w₁ρ_(m)(x,y)+w₂θ_(m)(x,y)}  (13)where w_(k), k=1,2 are the weighting factors and w₁+w₂=1. The advantageof this method is that the two coded apertures are made to havedifferent spatial frequency responses, and therefore do not produce thesame reconstruction artifacts, which are therefore at least partiallyaveraged out in the combination step 95, as defined in Eq. (13). Thisleads to a more robust determination of the range of each scene elementin step 93. In other arrangements, the weighting factors w_(k), shown inEq. (13) to be the same for each range value, vary between range values,subject to the constraint that w₁+w₂=1 for each range value. Theweighting factors w_(k) are predetermined to produce a combined imageset with a minimum of reconstruction artifacts, and depend on the choiceof coded apertures. This is accomplished through experimentation, orthrough optimization techniques known in the art.

In another arrangement, the reconstructed image sets 82 a, 82 b arecombined in the Fourier domain where the inverse Fourier transform istaken. In yet another arrangement, the combination is performed in theFourier domain using a spatial frequency dependent weighting criterion.This is computed using an expression such as:

$\begin{matrix}{{\hat{I}}_{comb} = \begin{Bmatrix}{{{{w_{1}\left( {v_{x},v_{y}} \right)}{{\hat{\rho}}_{1}\left( {v_{x},v_{y}} \right)}} + {{w_{2}\left( {v_{x},v_{y}} \right)}{\hat{\theta}}_{1}\left( {v_{x},v_{y}} \right)}};} \\{{{{w_{1}\left( {v_{x},v_{y}} \right)}{{\hat{\rho}}_{2}\left( {v_{x},v_{y}} \right)}} + {{w_{2}\left( {v_{x},v_{y}} \right)}{\hat{\theta}}_{2}\left( {v_{x},v_{y}} \right)}};} \\{{\ldots\mspace{14mu}{w_{1}\left( {v_{x},v_{y}} \right)}{{\hat{\rho}}_{m}\left( {v_{x},v_{y}} \right)}} + {{w_{2}\left( {v_{x},v_{y}} \right)}{{\hat{\theta}}_{m}\left( {v_{x},v_{y}} \right)}}}\end{Bmatrix}} & (14)\end{matrix}$where {circumflex over (ρ)}(v_(x),v_(y)) and {circumflex over(θ)}(v_(x),v_(y)) denote the Fourier transforms of ρ(x,y) and θ(x,y),respectively. The advantage of this method is that the two sets of codedaperture responses are weighted to have the most influence at spatialfrequencies where each aperture has an effective signal-to-noise ratio,such as away from zeroes in its Fourier response, which reducesreconstruction artifacts and produces more robust range estimates. Inthis arrangement, the weighting functions w₁(v_(x),v_(y)) andw₂(v_(x),v_(y)) obey the constraint w₁(0,0)+w₂(0,0)=1 for each rangevalue, in order to avoid changes in the overall brightness of theimages. Once again, the weighting factors w₁(v_(x),v_(y)) andw₂(v_(x),v_(y)) are predetermined to produce a combined image set with aminimum of reconstruction artifacts, and depend on the choice of codedapertures. This is accomplished through experimentation, or throughoptimization techniques known in the art. In this arrangement, theoptimization should take into account the spatial frequency dependenceof the weighting factors.

The deblurred image sets 81 a, 81 b are intentionally limited by using asubset of blur parameters from the stored set. This is done for avariety of reasons, such as reducing the processing time to arrive atthe range values 91, or to take advantage of other information from thecamera 40 indicating that the full range of blur parameters is notnecessary. The set of blur parameters used (and hence the deblurredimage sets 81 a, 81 b created) are limited in increment (i.e.subsampled) or extent (i.e. restricted in range). Returning to FIG. 10,the subset of blur parameters chosen need not be identical for the twocoded apertures. If not identical, this implies that there will not be aone-to-one correspondence between images in the reconstructed image sets82 a and 82 b for all or any range values. In some arrangements, the twocoded apertures are used to provide range estimates at range valueswhich are interleaved. In other arrangements, the two coded aperturesare used to provide range estimates at range values over disjointintervals. If a digital image sequence is processed, the set of blurparameters used are the same, or different for each image in thesequence.

Alternatively, instead of subsetting or subsampling the blur parametersfrom the stored set, reduced sets of deblurred images are created bycombining images corresponding to range values within selected rangeintervals. This might be done to improve the precision of depthestimates in a highly textured or highly complex scene which isdifficult to segment. For example, let z_(m), where m=1, 2, . . . Mdenote the set of range values at which the blur parameters [p₁, p₂, . .. p_(m)] and [q₁, q₂, . . . q_(m)] have been measured. Let î_(m)(x,y)denote the deblurred image corresponding to range value m and blurparameters p_(m), and let ĵ_(m)(x,y) denote the deblurred imagecorresponding to range value m and blur parameters q_(m) . Further, letÎ_(m)(v_(x),v_(y)) and Ĵ_(m)(v_(x),v_(y)) denote their Fouriertransforms. If the range values are divided into M equal groups orintervals, each containing M range values, reduced deblurred image setsis defined as follows:

$\begin{matrix}{{\hat{i}}_{red} = \begin{Bmatrix}{{\frac{1}{N}{\sum\limits_{m = 1}^{N}\;{{\hat{i}}_{m}\left( {x,y} \right)}}};{\frac{1}{N}{\sum\limits_{m = {N + 1}}^{2N}\;{{\hat{i}}_{m}\left( {x,y} \right)}}};} \\{{\frac{1}{N}{\sum\limits_{m = {{2\; N} + 1}}^{3N}\;{{\hat{i}}_{m}\left( {x,y} \right)}}};{\ldots\mspace{14mu}\frac{1}{N}{\sum\limits_{m = {{({N/M})} - N}}^{N/M}\;{{\hat{i}}_{m}\left( {x,y} \right)}}};}\end{Bmatrix}} & (15) \\{{\hat{j}}_{red} = \begin{Bmatrix}{{\frac{1}{N}{\sum\limits_{m = 1}^{N}\;{{\hat{j}}_{m}\left( {x,y} \right)}}};{\frac{1}{N}{\sum\limits_{m = {N + 1}}^{2N}\;{{\hat{j}}_{m}\left( {x,y} \right)}}};} \\{{\frac{1}{N}{\sum\limits_{m = {{2\; N} + 1}}^{3N}\;{{\hat{j}}_{m}\left( {x,y} \right)}}};{\ldots\mspace{14mu}\frac{1}{N}{\sum\limits_{m = {{({N/M})} - N}}^{N/M}\;{{\hat{j}}_{m}\left( {x,y} \right)}}};}\end{Bmatrix}} & (16)\end{matrix}$

In other arrangements, the range values are divided into M unequalgroups, whereas in other arrangements a different number of groups isassociated with each coded aperture. In yet another arrangement, areduced blurred image set is defined using a spatial frequency dependentweighting criterion via the following equation:

$\begin{matrix}{{\hat{I}}_{red} = \begin{Bmatrix}{{\frac{1}{N}{\sum\limits_{m = 1}^{N}\;{{w\left( {v_{x},v_{y}} \right)}{{\hat{I}}_{m}\left( {v_{x},v_{y}} \right)}}}};{\frac{1}{N}{\sum\limits_{m = {N + 1}}^{2N}{w\left( {v_{x},v_{y}} \right){{\hat{I}}_{m}\left( {v_{x},v_{y}} \right)}}}};} \\{{\ldots\mspace{14mu}\frac{1}{N}{\sum\limits_{m = {{({N/M})} - N}}^{N/M}{w\left( {v_{x},v_{y}} \right){{\hat{I}}_{m}\left( {v_{x},v_{y}} \right)}}}};}\end{Bmatrix}} & (17) \\{{\hat{J}}_{red} = \begin{Bmatrix}{{\frac{1}{N}{\sum\limits_{m = 1}^{N}\;{{w\left( {v_{x},v_{y}} \right)}{{\hat{J}}_{m}\left( {v_{x},v_{y}} \right)}}}};{\frac{1}{N}{\sum\limits_{m = {N + 1}}^{2N}{w\left( {v_{x},v_{y}} \right){{\hat{J}}_{m}\left( {v_{x},v_{y}} \right)}}}};} \\{{\ldots\mspace{14mu}\frac{1}{N}{\sum\limits_{m = {{({N/M})} - N}}^{N/M}{w\left( {v_{x},v_{y}} \right){{\hat{J}}_{m}\left( {v_{x},v_{y}} \right)}}}};}\end{Bmatrix}} & (18)\end{matrix}$where w(v_(x),v_(y)) is a spatial frequency weighting function. Such aweighting function is useful, for example, in emphasizing spatialfrequency intervals where the signal-to-noise ratio is most favorable,or where the spatial frequencies are most visible to the human observer.In some embodiments, the spatial frequency weighting function is thesame for each of the M range intervals, however, in other embodimentsthe spatial frequency weighting function is different for some or all ofthe intervals. In other arrangements, the spatial frequency weightingfunction is different for the two coded apertures.

FIG. 12 is a schematic of a digital camera system 400 in accordance withthe present invention. The digital camera system 400 includes an imagesensor 410 for capturing one or more images of a scene, a lens 420 forimaging the scene onto the sensor, a coded aperture plate 430 on whichis mounted at least two coded apertures, and a processor-accessiblememory 440 for storing a set of blur parameters derived from rangecalibration data for each coded aperture, all inside an enclosure 460,and a data processing system 450 in communication with the othercomponents, for providing a set of deblurred images using the capturedimages and each of the blur parameters from the stored set, and forusing the set of deblurred images to determine the range information forthe objects in the scene. The data processing system 450 is aprogrammable digital computer that executes the steps previouslydescribed for providing a set of deblurred images using captured imagesand each of the blur parameters from the stored set. In otherarrangements, the data processing system 450 is inside the enclosure460, in the form of a small dedicated processor.

The invention has been described in detail with particular reference tocertain preferred embodiments thereof, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention.

Parts List

-   s₁ Distance-   s₂ Distance-   s₁′ Distance-   s₂′ Image Distance-   P₁ On-Axis Point-   P₂ On-Axis Point-   P₁′ Image Point-   P₂′ Image Point-   D Diameter-   d Distance-   F Focal Plane-   R₀ Object Distance-   D₁ Planes-   D₂ Planes-   O₁, O₂, O₃ Scene Elements-   ρ₁, ρ₂, . . . ρ_(m) Element-   θ₁, θ₂, . . . θ_(m) Element-   I₁, I₂, . . . I_(m) Deblurred Image Set Element-   J₁, J₂, . . . J_(m) Deblurred Image Set Element-   10 Lens-   20 Axial ray-   22 Axial ray-   24 Axial ray-   26 Axial ray-   340 Lens-   32 Coded aperture-   34 Lens-   40 Image capture device-   42 Lens-   43 Coded aperture-   44 Mounting Plate-   45 Coded aperture-   46 Electronic sensor array-   47 a Blur parameters-   47 b Blur parameters-   48 Memory-   49 Digital representation of point spread functions-   50 Provide image capture device step-   60 Store blur parameters step-   70 Capture image step-   72 Captured image-   80 Provide set of deblurred images step-   81 a Deblurred image set-   81 b Deblurred image set-   82 a Reconstructed image set-   82 b Reconstructed image set-   90 Determine range information step-   91 Range information-   92 Convolve deblurred images step-   93 Compare scene elements step-   95 Combine reconstructed image sets step-   102 Receive blurred image step-   104 Initialize candidate deblurred image step-   105 Receive blur kernel step-   106 Blur kernel-   107 Candidate deblurred image-   108 Compute differential images step-   109 Differential images-   110 Compute combined differential image step-   111 Combined differential image-   112 Update candidate deblurred image step-   113 New candidate deblurred image-   114 Convergence test-   115 Convergence criterion-   116 Deblurred image-   117 Store deblurred image step-   200 Light source-   210 Condenser optics-   220 Light ray-   230 Light ray-   300 Array of indices-   310 Current pixel location-   400 Digital camera system-   410 Image sensor-   420 Lens-   430 Coded aperture plate-   440 Memory-   450 Processor-   460 Enclosure

The invention claimed is:
 1. A method of using an image capture deviceto identify range information for objects in a scene, comprising: a)providing an image capture device having an image sensor, at least twocoded apertures having different spatial frequency responses, and alens; b) storing in a memory a set of blur parameters derived from rangecalibration data for each coded aperture; c) capturing images of thescene having a plurality of objects using each of the coded apertures;d) providing a set of deblurred images using the captured images fromeach coded aperture and each of the blur parameters from the stored set;and e) using the set of deblurred images to determine the rangeinformation for the objects in the scene.
 2. The method of claim 1wherein step d) includes for each deblurred image i) initializing acandidate deblurred image; ii) determining a plurality of differentialimages representing differences between neighboring pixels in thecandidate deblurred including a range of at least 1 or more pixels insize and including vertical, horizontal and diagonal directions; iii)determining a combined differential image by combining the differentialimages; iv) updating the candidate deblurred image responsive to thecaptured image, the blur kernel, the candidate deblurred image and thecombined differential image; and v) repeating steps i)-iv) until aconvergence criterion is satisfied.
 3. The method of claim 1, whereinstep c) includes capturing first and second image sequences,corresponding to the first and second coded apertures, respectively. 4.The method of claim 3, wherein step e) includes determining rangeinformation for each image in the sequence.
 5. The method of claim 4,wherein step e) includes determining range information for a subset ofimages in the sequence.
 6. The method of claim 4, wherein the rangeinformation is used to identify stationary and moving objects in thescene.
 7. The method of claim 6, wherein the range information is usedby the image capture device to track moving objects.
 8. The method ofclaim 3, wherein the step of initializing a candidate deblurred imageincludes: a) determining a difference image between the current andprevious image in the image sequence; and b) initializing a candidatedeblurred image responsive to the difference image.
 9. The method ofclaim 8, wherein step e) includes determining range information for theobjects in the scene, responsive to the difference image.
 10. The methodof claim 1, wherein step d) includes using a subset of blur parametersfrom the stored set.
 11. The method of claim 1, wherein step b) includesusing a set of blur parameters derived from calibration data at a set ofrange values, such that there is a set of blur parameters for each codedaperture at each corresponding range value.
 12. The method of claim 1,wherein step b) includes storing a subset of blur parameters derivedfrom calibration data at a set of range values, responsive toinformation provided by the image capture device.
 13. The method ofclaim 1, wherein step e) includes combining the deblurred imagesaccording to a spatial-frequency dependent weighting criterion.
 14. Themethod of claim 1, wherein step e) includes combining deblurred imagesresulting from blur parameters corresponding to each coded aperturewithin selected range intervals.
 15. The method of claim 14, furtherincluding combining the deblurred images according to aspatial-frequency dependent weighting criterion.
 16. A digital camerasystem comprising: a) an image sensor for capturing one or more imagesof a scene; b) a lens for imaging the scene onto the image sensor; c) atleast two coded apertures having different spatial frequency responses;d) a processor-accessible memory for storing a set of blur parametersderived from range calibration data; and e) a data processing system forproviding a set of deblurred images using captured images and each ofthe blur parameters from the stored set by, i) initializing a candidatedeblurred image; ii) determining a plurality of differential imagesrepresenting differences between neighboring pixels in the candidatedeblurred image; iii) determining a combined differential image bycombining the differential images; iv) updating the candidate deblurredimage responsive to the captured image, the blur kernel, the candidatedeblurred image and the combined differential image; v) repeating stepsi)-iv) until a convergence criterion is satisfied; and vi) using the setof deblurred images to determine the range information for the objectsin the scene.
 17. A method of using an image capture device to identifyrange information for objects in a scene, comprising: a) providing animage capture device having an image sensor, at least two codedapertures having different spatial frequency responses, and a lens; b)storing in a memory a set of blur parameters derived from rangecalibration data for each coded aperture, using a set of blur parametersderived from calibration data at a set of range values, such that thereis a set of blur parameters for each coded aperture at eachcorresponding range value, wherein the sets of blur parameterscorresponding to each coded aperture are associated with overlappingrange intervals; c) capturing images of the scene having a plurality ofobjects using each of the coded apertures; d) providing a set ofdeblurred images using the captured images from each coded aperture andeach of the blur parameters from the stored set; and e) using the set ofdeblurred images to determine the range information for the objects inthe scene.
 18. A method of using an image capture device to identifyrange information for objects in a scene, comprising: a) providing animage capture device having an image sensor, at least two codedapertures having different spatial frequency responses, and a lens; b)storing in a memory a set of blur parameters derived from rangecalibration data for each coded aperture, using a set of blur parametersderived from calibration data at a set of range values, such that thereis a set of blur parameters for each coded aperture at eachcorresponding range value wherein the sets of blur parameterscorresponding to each coded aperture are associated with non-overlappingrange intervals; c) capturing images of the scene having a plurality ofobjects using each of the coded apertures; d) providing a set ofdeblurred images using the captured images from each coded aperture andeach of the blur parameters from the stored set; and e) using the set ofdeblurred images to determine the range information for the objects inthe scene.